update finetune cfg

minigpt4/configs/datasets/mvtec/default.yaml

@@ -3,4 +3,4 @@ datasets:
     data_type: images
     build_info:
       image_path: ./MVTEC_det/images
-      ann_path: ./MVTEC_det/mvtech_ad_data_for_regression.json
+      ann_path: ./MVTEC_det/train_data.json

minigpt4/datasets/datasets/mvtec_dataset.py

@@ -29,15 +29,17 @@ class MVTecDataset(Dataset):
     def __getitem__(self, index):
         info = self.ann[index]
         gt_bbox = info["bbox"]
+        ans_cls = info["class"]
 
         image_path = os.path.join(self.vis_root, info['image_path'])
         image = Image.open(image_path).convert("RGB")
         image = self.vis_processor(image)
 
-        input = "detect defect or non-defect and return the bounding box"
+        input = "detect a defect or not-defect object and return the bounding boxes and its label. If not, bound around the object."
         
-        ans_cls = "defect" if info["is_broken"] == True else "non-defect"
-        answer = f"{ans_cls}<{gt_bbox[0]}><{gt_bbox[1]}><{gt_bbox[2]}><{gt_bbox[3]}>"
+        ans_defect = "defect" if info["is_broken"] == True else "non-defect"
+        ans_para = f"<p>{ans_cls}-{ans_defect}</p>"
+        answer = f"{ans_para}<{gt_bbox[0]}> <{gt_bbox[1]}><{gt_bbox[2]}><{gt_bbox[3]}>"
 
         instruction = random.choice(self.instruction_pool).format(input)
         instruction = "<Img><ImageHere></Img> {} ".format(instruction)

train_configs/minigptv2_finetune_mvtec.yaml

@@ -32,10 +32,10 @@ run:
   warmup_lr: 1e-6
 
   weight_decay: 0.05
-  max_epoch: 10
+  max_epoch: 5
   num_workers: 6
   warmup_steps: 1000
-  iters_per_epoch: 1000
+  iters_per_epoch: 2000
 
   seed: 42
   output_dir: "mvtec_outputs"
@@ -51,5 +51,5 @@ run:
   dist_url: "env://"
   distributed: True
 
-  wandb_log: True
+  wandb_log: False
   job_name: minigptv2_finetune